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Policy and Action Standard
Agriculture, Forestry, and Other Land Use (AFOLU)
Sector Guidance
Draft, May 2015
2
Contributors (alphabetically)
Matthew Brander, University of Edinburgh
Jason Funk, Environmental Defense Fund
Apurba Mitra, World Resources Institute
Stephen Roe (lead), Center for Climate Strategies
Stephen Russell, World Resources Institute
Marion Vieweg, Current Future
Introduction
This document provides sector-specific guidance to help users implement the GHG Protocol Policy and
Action Standard in the Agriculture, Forestry, and Other Land Use (AFOLU) sector. The AFOLU category
combines the two sectors: LULUCF (Land Use, Land Use Change and Forestry) and Agriculture. Land
use and management influence a variety of ecosystem processes that affect greenhouse gas fluxes such
as photosynthesis, respiration, decomposition, nitrification/denitrification, enteric fermentation, and
combustion. These processes involve transformations of carbon and nitrogen that are driven by the
biological (i.e., activity of microorganisms, plants, and animals) and physical processes (combustion,
leaching, and run-off). The key greenhouse gases of concern are CO2, N2O and CH4. The AFOLU sector
faces some unique challenges with respect to GHG accounting. There are many processes leading to
emissions and removals of greenhouse gases, which can be widely dispersed in space and highly
variable in time. The factors governing emissions and removals can be both natural and anthropogenic
(direct and indirect) and it can be difficult to clearly distinguish between causal factors.
Users should follow the requirements and guidance provided in the Policy and Action Standard when
using this document. The chapters in this document correspond to the chapters in the Policy and Action
Standard. This document refers to Chapters 5–11 of the Policy and Action Standard to provide specific
guidance for the AFOLU sector. The other chapters have not been included as they are not sector-
specific, and can be applied to the AFOLU sector without additional guidance. Chapters 1–4 of the Policy
and Action Standard introduce the standard, discuss objectives and principles, and provide an overview
of steps, concepts, and requirements. Chapters 12–14 of the Policy and Action Standard address
uncertainty, verification, and reporting. The table, figure, and box numbers in this document correspond to
the table, figure, and box numbers in the standard.
To illustrate the various steps in the standard, this guidance document uses a running example of a
hypothetical boreal forest reforestation policy.
We welcome any feedback on this document. Please email your suggestions and comments to David
Rich at [email protected].
3
Table of Contents
Chapter 5: Defining the policy or action ........................................................................................................ 4
Chapter 6: Identifying effects and mapping the causal chain ..................................................................... 11
Chapter 7: Defining the GHG assessment boundary ................................................................................. 14
Chapter 8: Estimating baseline emissions .................................................................................................. 18
Chapter 9: Estimating GHG effects ex-ante ................................................................................................ 24
Chapter 10: Monitoring performance over time .......................................................................................... 28
Chapter 11: Estimating GHG effects ex-post .............................................................................................. 31
4
Chapter 5: Defining the policy or action
In this chapter, users are required to clearly define the policy or action that will be assessed, decide
whether to assess an individual policy or action or a package of related policies or actions, and choose
whether to carry out an ex-ante or ex-post assessment.
5.1 Select the policy or action to be assessed
Table 5.1 provides a non-exhaustive list of examples of policies and actions in the sector for which this
guidance document will be useful by policy/action type.
Table 5.1a Examples of policies/actions in the forestry sector by policy/action type
Type of policy or action Examples
Regulations and standards
Regulations aimed at reducing the proximate causes of deforestation, such as regulating against conversion of forest land to agricultural use (e.g. Indonesia Forest Moratorium, which prohibits new permits for palm plantations on peatland and primary forest; and Brazil’s Forest Code, which requires that landowners maintain a percentage of their land as forest).
Regulations aimed at reducing the underlying drivers of deforestation, such as regulating against the import of illegally sourced timber (e.g. the EU Timber Regulations, which prohibit the placement of illegal timber and timber products on the EU market). Often deforestation occurs where existing legislation is not well enforced, and policies to enforce existing legislation can be effective in reducing deforestation.
Enabling measures, such as the establishment or enforcement of land tenure rights (where insecure land tenure leads to deforestation), and land-use planning (e.g. Indonesia’s One Map establishes a single map of land categorizations and jurisdictions to avoid conflicting land use claims).
Taxes and charges -
Subsidies and incentives
Payments for reducing deforestation, or for afforestation/reforestation Examples:
China’s Sloping Land Conversion Program which involved payments for reforestation/afforestation on over 10 million hectares of sloping or degraded land;
The US Conservation Reserve Program, which is an incentive payment program for retiring land from agricultural use, and planting alternative vegetative cover;
The Norway-Guyana Partnership on Climate and Forests which involves results-based payments for reduced emissions from deforestation and forest degradation (REDD+).
Tradable permits -
5
Type of policy or action Examples
Voluntary agreements
Voluntary agreements involve entities agreeing to undertake actions for reducing deforestation, or for undertaking afforestation/reforestation (e.g. Voluntary Partnership Agreements under the Forest Law Enforcement, Governance and Trade (FLEGT) Action Plan involve partner countries voluntarily introducing certification schemes for legally harvested timber).
Information instruments
Information instruments that allow consumers to choose products which avoid deforestation (e.g. Forest Stewardship Council certification, Roundtable on Sustainable Palm Oil certification, and Roundtable on Sustainable Soy certification).
Research and development (R&D)
Research and development within the agricultural sector can be used to increase yields and reduce the expansion of agriculture into forested areas (or increase the amount of set-aside land available for afforestation/reforestation). For example, TSH Resources Bhd and the Malaysian Palm Oil Board have invested in developing an oil palm clone which may achieve yields of 10 tonnes crude palm oil/hectare compared to an average in Malaysia of 4.5 tonnes crude palm oil/hectare.
Public procurement policies
-
Infrastructure programs -
Implementation of new technologies, processes, or practices
The deployment of new technologies and practices within the agricultural sector can be used to reduce the expansion of agriculture into forested areas (or increase the amount of set-aside land available for afforestation/reforestation). Measures which increase agricultural yields should reduce the demand for new agricultural land (e.g. International Plant Nutrition Institute’s Best Management Practice (BMP) pilot plots illustrate the potential yields from implementing BMPs).
Financing and investment
Financing for improved agricultural productivity can reduce the expansion of agriculture into forested areas (or increase the amount of set-aside land available for afforestation/ reforestation). For example, the Indonesian Government has implemented a Plantation Revitalization Program, which provides improved seeds and low interest credit to support plantation owners during the period between replanting and when new trees reach maturity and produce a crop.
6
Table 5.1b Examples of policies/actions in the agriculture sector by policy/action type
Type of policy or action Examples
Regulations and standards
Regulations on limits of total applied nitrogen and the enforcement of closed periods for the application of slurries and manures (e.g., E.U. Nitrates Directive)
Production standards for concentrated production operations, such as feedlots
Zoning regulations for the expansion of agriculture (e.g., Brazil’s National Agro-Ecologic Zoning Program)
Taxes and charges
Agriculture can be affected by multi-sectoral/economy-wide polices such as cap and trade.
There is a generally a reluctance to use carbon taxes as a policy instrument in the agriculture sector. However, the sector is a significant user of fossil fuels (mostly through production of fertilizers, but also through the direct use of fossil fuels on farms), so would be affected by fuel taxes
Output taxes that differentially tax agricultural products based on their GHG intensity (e.g., beef products are levied with a higher tax)
Subsidies and incentives
Payments for foregone income from: setting aside agricultural land as buffer strips and arable field corners; entering land into agricultural conservation easements; and preserving woodland and wetlands.
Payments for changes in existing production practices (e.g., adoption of conservation tillage, enhanced hedgerow management, enhanced nutrient management, etc.).
Subsidies for increasing production of goods viewed as less GHG-intensive (e.g., bioenergy crops)
Subsidies for the development of on-farm sources of energy (e.g., biodigesters)
Tradable permits
Nutrient trading programs focused on specific watersheds
The linking of on-farm renewable energy generation with renewables obligations ( e.g., farmers selling RE certificates into a mandatory market)
Voluntary agreements
Conservation easements (linked to possible tax benefits or direct payments)
Voluntary reporting of agricultural GHG data (e.g., the now defunct DOE 1605(b) program included guidance for agriculture and forestry)
Information instruments Direct provision of training and advice to farmers on adoption of
GHG mitigation measures; extension services; etc.
Research and development (R&D)
Research programs targeting major emissions sources. Some examples: Enteric fermentation: improved livestock genetics, methanogen inhibitors, vaccine development, diet manipulation, etc.
Soil N2O: nitrification inhibitors, plant breeding/selection, technologies/practices that lower the N2O/N2 ratio during nitrification, etc.
Public procurement policies
-
Infrastructure programs -
7
Type of policy or action Examples
Implementation of new technologies, processes, or practices
Linked to R&D activities above
Financing and investment
Low-interest loans for the adoption of GHG mitigation practices
Loan guarantees and payments for energy audits, energy efficiency improvements, installation of renewable energy systems, etc.
5.2 Clearly define the policy or action to be assessed
A key step in Chapter 5 is to clearly define the policy or action. Chapter 5 in the standard provides a
checklist of information users should report. Table 5.2 provides an example of providing the information in
the checklist using the example of a hypothetical boreal forest reforestation policy.
Table 5.2 Checklist of information to describe the example policy
Information Example
The title of the policy or action
Boreal Forest Reforestation
Type of policy or action Implementation of new technologies, processes, or practices
Description of the specific interventions included in the policy or action
The policy goals call for reforestation of 5% of high site class lands by 2010; 15% by 2015; and 25% by 2025. “Site class” refers to forest areas impacted by wildfire. A high site class is an area that experienced the highest burn severity1.
The status of the policy or action
Accepted by the Climate Change Mitigation Advisory Group
Date of implementation 2010
Date of completion (if applicable)
N/A
Implementing entity or entities
Government
Objective(s) of the policy or action
Reforestation of high site class lands spurs higher levels of carbon sequestration since these areas will not go through the expected successional phases of grassland to mixed hardwood to conifer (lasting many decades). In particular, grasslands often dominate a high-severity burn area for many years which limits carbon sequestration potential. The policy intervention here is to bypass this grassland successional phase in burn areas dominated by grasses through replanting with mixed hardwood species. These mixed hardwood stands provide much higher sequestration potential than grasslands.
Geographical coverage State of Alaska boreal forests
Primary sectors, subsectors, and emission
Terrestrial carbon sequestration
1 The objective of the assessment is to estimate the level of emission reductions achievable if policy goals
are achieved.
8
Information Example
sources or sinks targeted
Greenhouse gases targeted
Carbon dioxide
Other related policies or actions
Forest management policies addressing thinning or other treatment of burn areas.
Optional information
Key performance indicators
Annual reforested area, forest biomass (forest carbon)
Intended level of mitigation to be achieved and/or target level of other indicators
Based on historical wildfire data, on average, 260,000 acres of high-severity burn area is created each year in the boreal forest. The table below shows the reforestation targets for the policy based on the goals stated above. Over 15 years, a total of nearly 700,000 acres would be reforested.
Boreal Forest Reforestation Targets
Year Acres
Replanted
Incremental C Accumulated
(tCO2)
2010 13,152 30,757 2011 18,413 43,060 2012 23,674 55,363 2013 28,935 67,666 2014 34,196 79,969 2015 39,457 92,272 2016 42,087 98,424 2017 44,718 104,575 2018 47,348 110,727 2019 49,979 116,878 2020 52,609 123,030 2021 55,240 129,181 2022 57,870 135,333 2023 60,501 141,484 2024 63,131 147,636 2025 65,761 153,787
Totals 697,072 1,630,147
Title of establishing legislation, regulations, or other founding documents
Alaska Climate Change Action Plan, Policy Option FAW-1 “Forest Management for Carbon Sequestration”, Element D “Boreal Forest Reforestation After Fire or Insect and Disease Mortality”.
MRV procedures -
Enforcement mechanisms -
Reference to relevant guidance documents
Climate Change Action Plans
The broader context/significance of the policy or action
As described above, the policy intervention promotes higher levels of carbon sequestration (forest biomass accumulation) than would be experienced under business as usual (BAU or baseline) conditions in high-severity burn areas of Alaska’s boreal forests.
Outline of non-GHG effects or co-benefits of the policy or action
Improved wildlife habitat;
Future timber/other biomass harvest value (note that biomass
9
Information Example
removals were not considered during the quantification of net GHG benefits);
Employment opportunities;
Reduced erosion in riparian areas.
Other relevant information -
5.3 Decide whether to assess an individual policy/action or a package of policies/actions
Chapter 5 also provides a description of the advantages and disadvantages of assessing an individual
policy/action or a package of policy actions. Steps to guide the user in making this decision based on
specific objectives and circumstances include identifying other related policies/actions that interact with
the initial policy/action.
The user would need to undertake a preliminary analysis to understand the nature of these interactions
and determine whether to assess an individual policy/action or a package of policy actions. This analysis
can be brief and qualitative, since detailed analysis of interactions would be taken up in subsequent
chapters. An illustrative example for the boreal forest reforestation policy is provided below.
Table 5.5 Mapping policies/actions that target the same emission source(s)
Policy assessed
Targeted
emission
source(s)
Other policies/actions
targeting the same
source(s)
Type of
interaction
Degree of
interaction
Boreal Forest
Reforestation
Increased carbon
sequestration in
high-severity burn
areas
Changes to forest
harvest practices (e.g.
rotation schedules) to
achieve greater
sequestration levels
Neutral -
Any forest
management policy
that targets high-
severity burn areas in
the boreal forest of
Alaska
Counteracting Uncertain
Table 5.6 Criteria to consider for determining whether to assess an individual policy/action or a
package of policies/actions
Criteria Questions Guidance Evaluation
Use of
results
Do the end-users of the assessment results want
to know the impact of individual policies/actions,
for example, to inform choices on which individual
policies/actions to implement or continue
supporting?
If “Yes” then
undertake
an individual
assessment
Yes
Significant
interactions
Are there significant (major or moderate)
interactions between the identified policies/actions,
either overlapping or reinforcing, which will be
missed if policies/actions are assessed
individually?
If “Yes” then consider
assessing a package
of policies/actions No
10
Feasibility
Will the assessment be manageable if a package
of policies/actions is assessed? Is data available
for the package of policies/actions? Are policies
implemented by a single entity?
If “No” then undertake
an individual
assessment No
For ex-post assessments, is it possible to
disaggregate the observed impacts of interacting
policies/actions?
If “No” then consider
assessing a package
of
policies/actions
Yes
Recommendation for the boreal forest reforestation policy
The policy design which focuses on high-severity burn areas, limits the potential for interaction with other
policies, hence the policy can be analyzed individually.
11
Chapter 6: Identifying effects and mapping the causal chain
In this chapter, users are expected to identify all potential GHG effects of the policy or action and include
them in a map of the causal chain.
6.1 Identify potential GHG effects of the policy or action
Using reliable literature resources (such as those mentioned in Box A, combined with professional
judgment or expert opinion and consultations etc. users can develop a list of all potential GHG effects of
the policy or action and separately identify and categorize them in two categories: In-jurisdiction effects
(and sources/sinks) and out-of-jurisdiction effects (and sources/sinks). In order to do this, users may find
it useful to first understand how the policy or action is implemented by identifying the relevant inputs and
activities associated with the policy or action. For the given policy example, an illustrative list of indicators
and possible effects for the policy (by type) is provided below.
Table 6.1 Summary of inputs, activities, and effects for the example policy
Indicator
types Examples for boreal reforestation policy
Inputs Spending on staff and material in boreal forest reforestation activities using mixed
hardwood stock
Activities
Produce re-planting stock
Conduct forest plantings
Manage/survey reforested areas
Intermediate
effects
Trees planted
Emissions at nursery operations (energy and fertilizer consumption)
Transport of materials and manpower
Higher biomass accumulation over baseline conditions
GHG effects Increase in CO2, CH4, and N2O emissions
Terrestrial carbon sequestration
Quantitative information may not be available for all elements identified in the table at the point of
assessment and not all elements are relevant for the determination of the causal chain. However,
creating a comprehensive list will not only provide support for the identification of effects, but will also help
to design a robust performance monitoring (see Chapter 11).
In the next step a comprehensive list of expected effects, based on the understanding of the design of the
policy, is developed.
Table 6.2 Illustrative example of various effects for the example policy
Type of effect Effect
Intended effect Sequestration due to increase in biomass accumulation levels above
baseline
Unintended effect Increased emissions from nursery and planting operations
In-jurisdiction
effect
Upstream emissions due to increased electricity production for nursery
energy consumption
Upstream emissions due to increased fuel supply for nursery energy
consumption
Out-of-jurisdiction
effect Upstream emissions due to increased production of nutrients and
containers
Short-term effect Increase in emissions due to fuel consumption during site surveys
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Increase in emissions due to fuel consumption during site plantings
Long-term effect None identified
6.2 Identify source/sink categories and greenhouse gases associated with the GHG effects
Users are also expected to identify and report the list of source/sink categories and greenhouse gases
affected by the policy or action.
Table 6.3 Sources/sinks and greenhouse gases affected by the example policy
Source/sink category Description Examples of emitting
equipment or entity
Relevant
greenhouse
gases
Sequestration Sequestration due to increase
in biomass accumulation
levels above baseline
Biomass CO2
Production of nutrients
and containers
Production of nutrients and
containers Production units CO2, CH4, N2O
Electricity production Electricity production for
nursery energy consumption Power plants CO2, CH4, N2O
Fuel consumption Increased fuel supply for
nursery energy consumption Stock nursery CO2, CH4, N2O
Fuel consumption Fuel consumption during site
surveys
Survey and monitoring
equipment CO2, CH4, N2O
Fuel consumption Fuel consumption during site
plantings Site planting equipment CO2, CH4, N2O
6.3 Map the causal chain
Once effects have been identified, developing a map of the causal chain allows the user and relevant
stakeholders to understand in visual terms how the policy or action leads to changes in emissions. Figure
6.3 presents a causal chain for the example policy based on the effects identified above.
13
Figure 6.3 Mapping GHG effects by stage for the example policy
For this chapter, there are a number of sector-specific resources such as guidance documents, tools,
databases of projects etc. that can be referred to while brainstorming possible effects of policies in the
sector, however the extent of available literature and resources varies by policy type and geography.
Some examples of these resources are provided in the methods and tools database on the GHG Protocol
website, which can be filtered by sector. Most of these resources will not be applicable in their entirety,
however select sections of these resources could provide a preliminary basis for further brainstorming
and analysis.
14
Chapter 7: Defining the GHG assessment boundary
Following the standard, users are required to include all significant effects in the GHG assessment
boundary. In this chapter, users determine which GHG effects are significant and therefore need to be
included. The standard recommends that users estimate the likelihood and relative magnitude of effects
to determine which are significant. Users may define significance based on the context and objectives of
the assessment. The recommended way to define significance is “In general, users should consider all
GHG effects to be significant (and therefore included in the GHG assessment boundary) unless they are
estimated to be either minor in size or expected to be unlikely or very unlikely to occur”.
7.1 Assess the significance of potential GHG effects
Many agricultural practices can potentially mitigate GHG emissions, the most prominent of which are
improved cropland and grazing land management (leading to decreased N2O emissions and increased
soil carbon sequestration) and restoration of degraded lands and cultivated organic soils (leading to soil
carbon sequestration). Lower but still significant mitigation potential is provided by water and rice
management (reduced N2O and CH4 emissions), activities resulting in soil carbon sequestration, land use
change and agroforestry (primarily carbon sequestration), and livestock management and manure
management (reduced N2O and CH4 emissions). Estimates vary, but most of the global mitigation
potential of agriculture (about 89%) rests in soil carbon sequestration. About 9% and 2% rests in reducing
methane and soil N2O emissions, respectively.
Rebound effects are very prevalent, which lead to substitution of one type of GHG emissions with
another. For instance:
Measures taken to enhance soil carbon sequestration (e.g., no till-practices or increased
irrigation) can lead to increased soil N2O emissions
Wooded riparian buffer zones can increase carbon sequestration but lead to increased soil N2O
emissions, compared to field margins.
Aerating a manure lagoon to reduce CH4 emissions will increase N2O emissions.
Removal of straw from flooded rice paddies to reduce CH4 emissions can lead to the requirement
for more fertilizer and increased N2O emissions.
The application of N-transformation inhibitors to soils to reduce the leaching of some N2O
precursors may increase that of others.
These cases demonstrate the need to identify trade-offs and consider multiple sources and GHGs in
tandem when evaluating possible mitigation measures. A whole-systems approach avoids potentially ill-
advised policies based on preoccupation with one individual practice.
For the forestry sector specifically, displacement or leakage effects are likely to be significant for:
a. Avoided deforestation policies that do not also address the provision of the services/products that
would have been provided by the deforested land in the baseline.
b. Afforestation/reforestation policies that do not also address the provision of the services/products
that would have been provided by the afforested/reforested land in the baseline.
For the boreal forest reforestation policy, an illustrative assessment boundary is described below.
15
Table 7.3 Example of assessing each GHG effect separately by gas to determine which GHG
effects and greenhouse gases to include in the GHG assessment boundary for the example policy
GHG effect Likelihood Relative magnitude Included?
Sequestration due to increase in biomass accumulation levels above baseline
CO2 Very likely Major Included
Upstream emissions due to increased production of nutrients and containers
CO2 Very likely Minor Excluded
CH4 Very likely Minor Excluded
N2O Very likely Minor Excluded
Upstream emissions due to increased electricity production for nursery energy consumption
CO2 Very likely Minor Excluded
CH4 Very likely Minor Excluded
N2O Very likely Minor Excluded
Upstream emissions due to increased fuel supply for nursery energy consumption
CO2 Very likely Minor Excluded
CH4 Very likely Minor Excluded
N2O Very likely Minor Excluded
Emissions due to fuel consumption during site surveys
CO2 Very likely Moderate Included
CH4 Very likely Minor Excluded
N2O Very likely Minor Excluded
Emissions due to fuel consumption during site plantings
CO2 Very likely Moderate Included
CH4 Very likely Minor Excluded
N2O Very likely Minor Excluded
16
Figure 7.3 Assessing each GHG effect to determine which GHG effects to include in the GHG
assessment boundary: Boreal reforestation policy
17
Table 7.4 List of GHG effects, GHG sources and sinks, and greenhouse gases included in the GHG
assessment boundary for the example policy
GHG effect GHG sources GHG sinks Greenhouse gases
1 Sequestration due to increase
in biomass accumulation levels
above baseline
Sequestration due to
increase in biomass
accumulation levels above
baseline
N/A CO2
2 Emissions due to fuel
consumption during site
surveys
Fuel consumption during
site surveys N/A CO2
3 Emissions due to fuel
consumption during site
plantings
Fuel consumption during
site plantings N/A CO2
The 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4, Chapter 4, has default
values for above-ground biomass for different forest types, and also default carbon-density factors for
biomass. These default data can be used, together with estimates of the area of forest (either protected
or created), to estimate avoided emissions or the enhancement of sinks.
This approach can be used to estimate both the primary emission reductions/sink enhancements of a
policy, and also the magnitude of leakage effects (i.e. by estimating the displaced area of deforestation,
or foregone afforestation/reforestation).
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Chapter 8: Estimating baseline emissions
In this chapter, users are expected to estimate baseline emissions over the GHG assessment period from
all sources and sinks included in the GHG assessment boundary. Users need to define emissions
estimation method(s), parameter(s), driver(s), and assumption(s) needed to estimate baseline emissions
for each set of sources and sinks.
8.3 Choose type of baseline comparison
A challenge to applying the comparison groups approach for this sector would be identifying control
groups in other regions that offer analogous environmental conditions. GHG emissions in the sector are
heavily affected by environmental factors such as temperature, rainfall, soil pH, slope, etc.
8.4 Estimating baseline emissions using the scenario method
8.4.1 Define the most likely baseline scenario
Users need to identify other policies and non-policy drivers that affect emissions in the absence of the
policy or action. Examples of other policies and non-policy drivers are provided in Table 8.3 and Table
8.4.
Table 8.2 Examples of other policies or actions in the AFOLU sector that may be included in a
baseline scenario
Forestry Sector
Examples of other policies Sources of data for developing assumptions
Level of enforcement of protected areas No central source and specific to each country.
Permitting for land conversion No central source and specific to each country.
Biofuel policies (e.g. EU Renewable Energy
Directive, and US Renewable Fuel Standard)
No central source and specific to each country.
Agricultural production subsidies www.fao.org and www.ifpri.org
Agriculture Sector
Examples of other policies
Sources of data for
developing
assumptions
North America Environmental Quality Incentives Program (EQIP)—cost-
sharing and incentive payments for conservation practices on working
farms (USA)
Equilibrium models
forecasting
commodity/input prices
and land demand
The Natural Resources Conservation Service (NRCS) – rewards and
recognizes actions that provide GHG benefits – improved N use efficiency
rewarded (USA)
The Conservation Reserve Program (CRP)—environmentally sensitive
land converted to native grasses, wildlife plantings, trees, filter strips,
riparian zones (USA)
The Conservation Security Program (CSP)—(voluntary) assistance
promoting conservation on cropland, pasture, and range land (and farm
woodland) (USA)
Greencover in Canada and provincial initiatives—encourages shift from
annual to perennial crop production on poor quality soils (Canada -
defunct)
19
USDA renewable energy initiatives in 26 States (USA) - Provide loan
guarantees and payments for e.g., RE installations
EU CAP payments for set-asides
Laws on nutrient management and water quality (e.g., EU Water
Framework Directive, Water codes of the Russian Federation, etc.)
Bans on agricultural activities (e.g., open burning of crop residues) that
impair air quality
EU ban on dumping at sea of sewage sludge, leading to more sewage
being applied on farms
Regulations to promote conversion of degraded lands to set-asides or
more productive agricultural land (e.g., eastern Europe and China)
Table 8.4 Examples of non-policy drivers that may be included in a baseline scenario
Forestry Sector
Non-policy drivers Sources of data for developing assumptions
Population growth World Bank:
http://data.worldbank.org/indicator/SP.POP.GROW
Economic growth World Bank:
http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG
Changing patterns in demand for agricultural
commodities (e.g. increased demand for
animal protein in developing countries)
www.fao.org and www.ifpri.org
Agricultural yields www.fao.org
Agriculture Sector
Non-policy drivers Sources of data for developing assumptions
Shifts in consumer preferences (e.g., growing
demand for meat) Surveys
Changes in prices of energy and agricultural
commodities National statistics
Changes in weather and climate Climate models
Water availability National statistics
Changes in use of land base (e.g.,
urbanization, deforestation, agricultural
intensification/industrialization) due to
demographic/economic changes and
advances in technology
Equilibrium models, land cover data sets
8.4.2 Select a desired level of accuracy
There are different methodological choices related to the level of accuracy of an assessment. Simplified
methods can be used, such as IPCC Tier 1 methods, or more complex methods, such as IPCC Tier 3.
The methods by which the parameter values of the selected method are derived also impacts the
accuracy of the analysis. A further important factor is the source of data, where internationally applicable
default values constitute lower levels of accuracy than jurisdiction or source specific data.
Further, emission factors can be static (calculated upfront and applied for the duration of the assessment)
or dynamic (updated over time to reflect changes in recycling, compost, or electricity markets) and that
can be another means of making the distinction. A low accuracy method could have the option of applying
20
a static emission factor and higher accuracy methods could update emission factors on a regular basis to
maintain accuracy.
For the example of the boreal forest reforestation policy, examples for different levels of accuracy based
on the number of effects to include are provided below.
Low accuracy: Section 8.4.3 below provides an example for estimating only one effect: net carbon
sequestration. The calculation is provided for one year of reforestation projects (2010: totaling 13,152
acres).
Although not conducted for this example, an intermediate accuracy assessment could also capture
energy consumption related emissions for initial surveys and periodic monitoring, as well as the energy
consumed due to transport materials and personnel to reforestation sites. Data would need to be
gathered from state forestry experts, including the mode (air or road) and distance of transport, and
schedule for periodic monitoring. After estimating the annual vehicle or air kilometers of travel, literature
data or refined transport models (e.g. the U.S. EPA’s MOVES model) could be used to determine fuel
consumption (e.g. diesel and/or aviation gasoline). Standard IPCC emission factors could then be used to
estimate emissions of CO2, CH4, and N2O.
High accuracy: A high accuracy assessment would also include additional energy consumed during re-
planting stock production and the upstream GHG emission estimates for fuel consumption, nutrient
consumption, and electricity consumption. Upstream GHG emission factors for fuels (addressing
extraction, processing/refining, and distribution) would need to come from literature sources or available
models. In the U.S., the Department of Energy, Argonne National Laboratory (ANL) and several state
agencies maintain models for estimating these full energy-cycle emissions (e.g. ANL’s GREET Model).
Use of energy and fertilizer for planting stock at nurseries, as well as information on the upstream
emissions for fertilizer consumption would need to be obtained through a review of the current literature.
For any electricity consumed, existing protocols, such as The Climate Registry’s General Reporting
Protocol (covering North America), as well as emission factor databases such as eGRID for the United
States would be a source of information for the carbon intensity of grid-based electricity.
8.4.3 Define the emissions estimation method(s) and parameters needed to calculate baseline
emissions
The annual carbon stock changes for the entire AFOLU sector can be estimated as the sum of changes
in all land-use categories:
Equation 1 Estimating carbon stock changes for the AFOLU sector
OLSLWLGLCLFLAFOLU CCCCCCC
Where:
ΔC = carbon stock change
AFOLU = Agriculture, Forestry and Other Land Use
FL = Forest Land
CL = Cropland
GL = Grassland
WL = Wetlands
SL = Settlements
OL = Other Land
21
As an example, the baseline calculation method for emissions associated with the sequestration due to
increase in biomass accumulation levels (one of the identified sources in the assessment boundary of the
policy example of boreal forest reforestation) is demonstrated below:
Baseline boreal grassland CO2 sequestration:
Equation 2 Estimating baseline emissions for net carbon sequestration
Baseline emissionsyear = (Replanted area x carbon accumulation rate by cover type x 44/12) x (-1)
Table A2 Examples of determining baseline values from published data sources
Parameter Sources of published data for baseline values
Carbon accumulation rate
by cover type
IPCC Guidelines, national forestry agencies, national/local studies, local
measurement
Replanted area (Average
wildfire activity) National/regional statistics
Table 8.2 Examples of typical other policies and actions, and related data sources for developing
assumptions (for developing new baseline values) for each parameter
Parameter Relevant polices Sources of data for developing
assumptions
No other policies were identified for this reforestation example.
Table 8.4 List of typical non-policy drivers and related data sources for developing assumptions
(for developing new baseline values) for each parameter
Parameter Typical non-policy drivers Sources of data for developing
assumptions
Replanted Area Climate change-induced drivers,
including increases in wildfire
occurrence in areas affected by
severe burns are important.
Secondary sources of data including
state/provincial natural resource
inventories, greenhouse gas
inventories, or regional planning
organizations (e.g. air quality
planning organizations).
8.4.4 Estimate baseline values for each parameter
The following table provides an overview of the parameter values used for the baseline calculation.
2 Table numbering differs, as there is no corresponding table included in the standard. The table is adapted from table 8.7 in the standard.
22
Table 8.7 Example of reporting parameter values and assumptions used to estimate baseline
emissions for the food waste diversion policy
Parameter
Baseline value(s) applied over the GHG assessment period
Methodology and assumptions to estimate value(s)
Data sources
Carbon accumulation rate
0.010 tC/acre-yr (boreal grassland)
The assumption is that without the policy,
area affected by wildfires would be covered
by boreal grassland.
IPCC above-ground biomass value for boreal
grasslands is 1.7 t dry mass/hectare:
http://www.ipcc-
nggip.iges.or.jp/public/2006gl/pdf/4_Volume4
/V4_06_Ch6_Grassland.pdf.
Assume dry mass is 50% carbon by weight; 35 years time for mixed hardwood forest to reach maturity (grassland likely reaches maturity well before 35 years).
IPCC 2006 Guidelines
Mixed hardwood sequestration rate
0.648 tC/acre-yr Unpublished value: Assumes forest regeneration with Balsam Poplar, which yields 30 cords/acre over 35 years.
AK Division of Forestry staff communication.
Replanted area
Boreal Forest Reforestation Targets
Year Acres
Replanted
Incremental C
Accumulated
(tCO2)
2010 13,152 30,757
2011 18,413 43,060
2012 23,674 55,363
2013 28,935 67,666
2014 34,196 79,969
2015 39,457 92,272
2016 42,087 98,424
2017 44,718 104,575
2018 47,348 110,727
2019 49,979 116,878
2020 52,609 123,030
2021 55,240 129,181
Targets set
23
2022 57,870 135,333
2023 60,501 141,484
2024 63,131 147,636
2025 65,761 153,787
Totals 697,072 1,630,147
8.4.5 Estimate baseline emissions for each source/sink category
The final step is to estimate baseline emissions by using the emissions estimation method identified in
Section 8.4.3 and the baseline values for each parameter identified in Section 8.4.4.
Baseline emissions2010 = (Replanted area x carbon accumulation rate x 44/12) x (-1)
= (13,152 acres x 0.010 tC/acre-yr x 44 tCO2/12 tC) x (-1)
= - 482 tCO2
The same calculations would need to be made for each year of the policy assessment period addressing
this first year of reforestation projects and adding in the cumulative sequestration for the additional area
reforested each year.
8.6 Aggregate baseline emissions across all source/sink categories
Table 8.9 provides an illustrative example of the results of the analysis for all effects included in the
assessment boundary, assuming the calculation steps outlined in section 8.4, that were illustrated with
effect 1, were carried out for each of the effects.
Table 8.9 Example of aggregating baseline emissions for the boreal forest reforestation policy3
GHG effect included in the GHG
assessment boundary Affected sources Baseline emissions
1 Sequestration due to increase in
biomass accumulation levels
above baseline
Sequestration due to
increase in biomass
accumulation levels
above baseline
- 482 t CO2
2 Emissions due to fuel
consumption during site surveys
Fuel consumption during
site surveys 0
3 Emissions due to fuel
consumption during site
plantings
Fuel consumption during
site plantings 0
Total baseline emissions - 482 t CO2
Note: The table provides data for the end year in the GHG assessment period (2025).
3 Numbers for effects 2 and 3 are illustrative.
24
Chapter 9: Estimating GHG effects ex-ante
In this chapter, users are expected to estimate policy scenario emissions for the set of GHG sources and
sinks included in the GHG assessment boundary based on the set of GHG effects included in the GHG
assessment boundary. Policy scenario emissions are to be estimated for all sources and sinks using the
same emissions estimation method(s), parameters, parameter values, GWP values, drivers, and
assumptions used to estimate baseline emissions, except where conditions differ between the baseline
scenario and the policy scenario, for example, changes in activity data and emission factors.
9.2 Identify parameters to be estimated
Data needs for emissions estimation vary with the type of policy / action being implemented. For example,
the data needs for emissions estimation in case of afforestation and reforestation of lands (except
wetlands) include change in carbon stock in tree biomass, change in carbon stock in shrub biomass,
change in carbon stock in dead wood biomass, change in carbon stock in litter, change in carbon stock in
soil organic carbon (SOC) and increase in non-CO2 GHG emissions. The data needs for estimation of
N2O Emissions Reductions in Agricultural Crops through Nitrogen Fertilizer Rate Reduction (Approved
VCS Methodology VM0022) are:
Mass of project nitrogen (N) containing synthetic fertilizer applied,
Mass of project N containing organic fertilizer applied,
N content of project synthetic fertilizer applied,
N content of project organic fertilizer applied,
Emission factor for project N2O emissions from N inputs,
Project synthetic N fertilizer applied,
Project organic N fertilizer applied,
Fraction of all synthetic N added to project soils that volatilizes as NH3 and NOx,
Fraction of all organic N added to project soils that volatilizes as NH3 and NOx,
Fraction of N added (synthetic or organic) to project soils that is lost through leaching and runoff,
in regions where leaching and runoff occurs,
Emission factor for project N2O emissions from atmospheric deposition of N on soils and water
surfaces and
Emission factor for project N2O emissions from N leaching and runoff.
Table A in Chapter 8 forms the basis for determining which parameters are affected by the policy. In case
the determination of affected parameters is not straightforward, the methodology to determine
significance outlined in Chapter 7 can be used. For the first effect of the policy ‘sequestration’, the only
parameter from equation 2 affected by the policy is the carbon sequestration factor.
9.4 Estimate policy scenario values for parameters
Once the affected parameters are determined the parameter values for the policy scenario can be
determined. All other parameters remain as in the baseline scenario. Table 9.2 provides an example.
Table 9.2. Example of reporting parameter values and assumptions used to estimate ex-ante
policy scenario emissions for the boreal forest reforestation policy
Parameter Baseline
Value
Policy Scenario Values
Trend over time for scenario
value(s)
Time period of
effect Source(s)
used Comments / Explanation
Carbon accumulation
0.010 tC/acre-yr
0.648 tC/acre-yr
Unpublished value: Assumes
AK Division of Forestry staff
25
rate (boreal grassland)
(mixed hardwood)
forest regeneration with Balsam Poplar, which yields 30 cords/acre over 35 years.
communication.
Area reforested
See table 8.7
Same as baseline values
Not affected
9.5 Estimate policy scenario emissions
Once parameter values have been determined, the same equations as used for the calculation of baseline values can be used to derive the policy scenario values:
Policy intervention reforestation CO2 sequestration:
Policy scenario emissions2010 = (Replanted area x carbon accumulation rate by cover type x 44/12) x (-1)
= (13,152 acres x 0.648 tC/acre-yr x 44 tCO2/12 tC) x (-1)
= - 31,249 tCO2
9.6 Estimate the GHG effect of the policy or action (ex-ante)
After determining the GHG emissions for the policy scenario for each source category, the change resulting from the policy can be determined. Table 9.3 provides an overview of the results. Table 9.3 Example of estimating the GHG effect of the food waste diversion policy4
GHG effect included Affected sources
Policy
scenario
emissions
Baseline
emissions Change
1 Sequestration due to
increase in biomass
accumulation levels
above baseline
Sequestration due to
increase in biomass
accumulation levels
above baseline
- 31,249 tCO2 - 482 t CO2 - 30,767 tCO2
2 Emissions due to
fuel consumption
during site surveys
Fuel consumption
during site surveys 157 tCO2 0 157 tCO2
3 Emissions due to
fuel consumption
during site plantings
Fuel consumption
during site plantings 473 tCO2 0 473 tCO2
Total emissions /
Total change in
emissions
- 30,619 tCO2 - 482 t CO2 - 30,137 tCO2
Note: The table provides data for the end year in the GHG assessment period (2020).
The primary parameters are the area of re-forestation each year, the above-ground carbon stocks for
mature boreal grasslands and hardwood stands, and the time required for the new hardwood stands to
reach maturity. [Note that below ground carbon stocks (roots, soil carbon) could also be important in an
overall assessment of forest carbon benefits; however, good data for these carbon pools are often
4 Numbers for effects 2 and 3 are illustrative.
26
lacking. For the example policy, below ground stocks for a mature hardwood stand would be expected to
be higher than a grassland; therefore, the estimated GHG benefits are conservatively low].
The example provided uses a simplified method suitable for policy analysis. In addition to the area of
reforestation projects, a key parameter is the rate of biomass (carbon) accumulation. The simplified
method determines this annual average rate using an estimate of above-ground biomass density (e.g.
cubic meters/hectare) for a given forest type, and then divides by the number of years expected for that
reforested stand to reach maturity. In reality, the slope of accumulation is not linear as shown in the
example chart below for two pine species. During the first 5 to 10 years, biomass accumulation rates
would be expected to be fairly modest; once the stand is fully-established, then the accumulation rate
increases significantly over the succeeding 30-50 years, before beginning to level off. In cases where
more precise estimates are needed, use of a timber growth and yield model should be considered.
Source: Alavapati et al, 2002. http://www.sciencedirect.com/science/article/pii/S0921800902000125
Box B.1 Addressing policy interactions
There are likely to be overlapping effects between agricultural policies that reduce yields (as an
unintended effect) and policies that aim to reduce emissions from deforestation or forest degradation
(REDD+) – as reduced yields are likely to increase the total amount of agricultural land required. There
are likely to be reinforcing effects between agricultural policies that increase yields and policies that aim
to reduce emissions from deforestation or forest degradation (REDD+) – as increased yields are likely to
reduce the total amount of agricultural land required. Similarly there are likely to be interactions between
afforestation/reforestation policies that reduce agricultural production or the availability of agricultural
land, and REDD+ policies which aim to reduce deforestation – as restrictions in the supply of agricultural
commodities is likely to increase prices, and farmers may respond by converting more land to agricultural
use.
The example policy did not have any interactive policies identified. However, a hypothetical example
could be some promotion of timber harvests for either energy use or forest products (resulting in harvests
above baseline conditions). With the exception of durable wood products (e.g. lumber for building
structures, furniture), an increase above baseline for other forest product harvests should be subtracted
from the future estimated carbon sequestration (in the year harvested). This assumes that the replanted
27
stands are subject to future harvests. For example, based on the type of forest, time required for the
stand to reach marketable diameter, and anticipated harvesting procedures (e.g. clear cut versus select
removal); the amount of carbon in the above-ground live tree carbon pool harvested should be subtracted
out of the cumulative forest carbon sequestered by that year. Average annual sequestration rates over
that time period should then reflect only the net carbon remaining after harvests.
In the case of harvests for durable wood products, some fraction of the harvested biomass will remain
stored in that product. Forest project protocols, similar forestry guidance, or local forestry experts should
be consulted to establish a defensible fraction of carbon to be stored in these products (e.g. net of live
tree carbon harvested minus forest harvest residue minus forest products industry waste).
28
Chapter 10: Monitoring performance over time
In this chapter, users are required to define the key performance indicators that will be used to track
performance of the policy or action over time. Where relevant, users need to define indicators in terms of
the relevant inputs, activities, intermediate effects and GHG effects associated with the policy or action.
10.1 Define key performance indicators
Some typical indicators for common policies in the sector are shown in the table below.
Table 10.1 Examples of indicators
Moratorium on forest conversion
Enhanced forest management
Incentive payment for reforestation
Incentives for adoption of conservation tillage techniques (no-till, ridge-till and mulch-till, etc.)
Input indicators
Financial and human resources for monitoring and enforcement
Financial and human resources for enhanced forest management
Financial resources committed to program
Money, skills (agricultural extension services)
Activity indicators
Provision of moratorium map, and number of prosecutions for breeches in moratorium
Number of forest managers trained
Optimization studies on the volume and timing of fertilizer amendments
Amount of incentive payments dispersed
Methods used to cultivate agricultural soils
Intermediate effect indicators
Area of land use change
Area of forest land under improved management
Area of land with successfully established trees
Percentage of farmers using conservation tillage techniques or percentage of farmland under conservation tillage
GHG effects
Gross GHG emissions or removals from the moratorium area
Gross GHG emissions or removals from the forest land under management
Avoidance of the direct N2O emissions from soils; avoidance
Gross GHG emissions and removals from the land planted with trees
Avoided soil CO2 emissions and increased soil carbon sequestration
29
of indirect N2O emissions from the volaitization of N from soils and leaching/run-off of excess
Non-GHG effects
None identified
Employment generated
Revenue generated
Revenue generated
10.4 Create a monitoring plan
Although the example policy for Alaska did not provide details on monitoring, a monitoring program to
fully measure and document the GHG effects could be implemented. Detailed measurement and
monitoring procedures are available from forestry project offset protocols that could be adapted to monitor
the policy’s effects at the broader scale envisioned by the policy.
A monitoring program would need to address both baseline conditions (i.e. high site class lands that will
not be reforested) as well as replanted areas. This would involve establishing sample plots in both areas
that will be monitored over at least several decades. The number of sample plots required is a function of
the variability in initial carbon stocks and the level of precision needed for assessing GHG effects (see the
cited protocols for additional details on establishing sample plots). A measurement frequency should also
be established for both baseline and replanted sample plots. While project protocols might require this to
be done annually or every few years, for a state-level policy, such as the example policy, once every 3-5
years is probably sufficient.
For each sample plot, biomass (carbon stocks) is measured for each of the carbon pools: e.g. above-
ground live tree, standing dead trees, down dead trees, understory, forest litter, and possibly soil carbon.
Offset protocols used as a model to develop a monitoring program may also contain methods to account
for the carbon storage value of timber harvested during the monitoring period within durable wood
products.
After each monitoring cycle, the measurement of carbon stocks could be transformed into annual
estimates of carbon sequestered both within the baseline areas and the replanted areas. Offset protocols
will often specify appropriate forest growth models that should be applied. This transformation would
provide estimates of carbon sequestered per hectare/year, for example, in both baseline and replanted
areas. The net benefit of the policy is the total carbon sequestered within the replanted areas minus the
amount of carbon that would have been sequestered without the policy (i.e. baseline sequestration rate x
replanted area). Added to this amount would be any additional carbon stored in durable wood products
from timber harvests on replanted areas (this example assumes that the baseline areas would not also
produce marketable timber within the monitoring period).
For Tier 2 or 3 assessments, in addition to the forest carbon measurements described above, there will
be a need to maintain information on the energy consumed as a result of the replanting projects
implemented through the policy and the overall monitoring program. The resulting net GHG effects will be
the sum of carbon dioxide sequestered above baseline (a negative number) and the GHG emissions for
energy consumed during replanting and monitoring.
Taking the example of a high accuracy ex-post GHG assessment, an illustrative example of a monitoring
plan for the example policy is provided below.
Table 10.5. Example of information to be contained in the monitoring plan
30
Indicator or parameter (and unit)
Source of data Monitoring frequency
Measured/modelled/ calculated/estimated (and uncertainty)
Responsible entity
Baseline sequestration rate
Forest growth models in offset protocols
Once every 3-5 years
Modeled Implementing body
Replanted area Policy implementation plan
Once every 3-5 years
Measured Implementing body
Policy sequestration rate
Forest growth models in offset protocols
Once every 3-5 years
Modeled Implementing body
31
Chapter 11: Estimating GHG effects ex-post
A number of ex-post assessment methods have been described in this chapter, which can be classified
into two broad categories i.e. Bottom-up methods and top-down methods.
11.2 Select an ex-post assessment method
The applicability of individual ex-post quantification methods for the sector and illustrative sources of data
are discussed in Table 11.1.
Bottom up methods are more applicable to REDD+ policies: the most common approach used for REDD+
projects and results-based payments at a national level involves the use of remotely sensed data and
ground-based studies of land cover change (known as activity data) and data on carbon stocks for
different types of land cover (known as emission factors). These data are then used to calculate total
emissions from deforestation (removals from enhanced sinks). The impact of individual policies in
achieving the observed level of deforestation might be estimated by adjusting the observed level of
deforestation for other drivers in the baseline.
Table 11.1 Applicability of ex-post assessment methods in the forestry sector
Bottom up methods Applicability
Collection of data from affected
participants/ sources/other
affected actors
Applicable. Direct monitoring of emissions is not possible, but
direct monitoring of parameters is common.
Additionally, aggregated data for observed deforestation, or
area afforested/reforested, can be “cleansed” for background
“noise” (e.g. spikes on agricultural commodity prices, or civil
conflict) – alternatively, the baseline could be recalculated with
these observed drivers factored in.
Engineering estimates
An approach comparable to “engineering estimates” may be
applicable to improved forest management policies, where a
model for the enhancement of sinks may be used to estimate
the impact of introducing improved forest management
practices.
Deemed estimate Applicable
Methods that can be bottom-up
or top-down depending on the
context
Applicability
Stock modeling Applicable
Diffusion indicators Applicable
Top down methods Applicability
Monitoring of indicators Applicable
Economic modeling N/A
Bottom up methods are more applicable for the agriculture sector.
Table 11.1 Applicability of ex-post assessment methods in the agriculture sector
Bottom up methods Applicability
Collection of data from affected
participants/ sources/other
affected actors
Many, but not all, agricultural emissions sources can be
measured with in-situ measurement techniques (e.g.,
controlled livestock chambers for measuring enteric
32
fermentation and flux chambers for monitoring the amount of
N2O and/or CO2 emitted from plots of land). While useful for
research, direct measurements are generally too expensive to
be feasible for the purposes of ex-post evaluation. However,
they can be used to improve more approximate estimation
techniques, such as emissions factors, and to calibrate
mechanistic (biogeochemical process) models.
Engineering estimates
Biogeochemical process models link important biogeochemical
processes that control the production, consumption, and
emission of GHGs. They can account for the cumulative GHG
impacts of a suite of management practices and other
variables that affect GHG emissions, as long as the models are
applied under the conditions for which they were developed
(e.g., applied in regions and management systems for which
calibrating data are available). Often, more field data sets may
be required to support the implementation and expansion of
models in policy analyses. Also, it is paramount that detailed
consideration is given to the structural and input uncertainty
related to the use of these models. The aggregate accuracy of
models is likely to increase with spatial scale, as more
combinations of environmental conditions are averaged over.
Deemed estimate -
Methods that can be bottom-up
or top-down depending on the
context
Applicability
Stock modeling -
Diffusion indicators -
Top down methods Applicability
Monitoring of indicators -
Economic modeling -
For the policy example, quantifying effects ex-post will require the use of bottom-up monitoring data as
described above. Top-down data might include the total area reforested as a result of the policy; however,
that alone will not be sufficient to derive GHG effects. Estimates of carbon accumulation within each of
the forest carbon pools from on-site surveys will be needed in order to estimate the amount of carbon
dioxide sequestered annually.
Table 11.1 Applicability of ex-post assessment methods
Bottom up methods Applicability
Collection of data from affected
participants/ sources/other
affected actors Required as described
Engineering estimates Not applicable
Deemed estimate Not applicable
Methods that can be bottom-
up or top-down depending on
the context
Applicability
Stock modeling Required as described
Diffusion indicators Not applicable
Top down methods Applicability
Monitoring or indicators Monitoring of areas replanted will need to be combined with
33
bottom-up survey data
Economic modeling Not applicable
11.3 Select a desired level of accuracy
Examples of how to implement ex-post quantification methods using low to high accuracy level
approaches for the policy example are described below:
Low accuracy
A low accuracy approach for the example policy would focus on the net increase in carbon sinks achieved
by the policy. The approach could be based on a combination of on-site surveys and remote sensing
(satellite imagery or aerial photos) to assess the health of reforested areas. The results of a limited
number and/or frequency of forest biomass surveys would supply the carbon accumulation rates for
reforested areas, while the imagery would be used to assess the relative health of all reforested areas.
Intermediate accuracy
An intermediate accuracy approach would also focus on carbon sequestration, however, here the number
and frequency of on-site surveys would conform to a widely-accepted forest carbon accounting protocol.
Per the protocol requirements, survey results of monitoring plots would be scaled-up in each year and
used within the protocol’s accepted yield models or equations to estimate accumulated carbon (and
annual CO2 sequestration).
High accuracy
For a high accuracy approach, other GHG sources would be added to the Tier 2 method above in order to
provide a set of net sequestration values during each historical year. These additional sources could
include energy consumption during survey activities (e.g. fuel combustion during transport of survey
teams).
© 2015 World Resources Institute